نشریه دانش آب و خاک
سال بیستم شماره 2 (تابستان 1389)

In mountainous basins, snow water equivalent (SWE) estimation is an important step of water resources management. The main purpose of this research was the investigation of the weighting method efficiency using GIS in estimating spatial distribution of SWE. To this regard, using the observed snow depth and density, the SWE was initially calculated. Then, the weighting method was used to estimate spatial distribution of SWE. The results of the weighting method were evaluated by the interpolation technique map. Weighted SWE map indicated that very high and high SWE zones were dominant in areas with high elevations, moderate slope and high upwind slopes. In general, the results showed that weighting method is enable to estimate spatial distribution of SWE satisfactorily.

Use of mathematical models as efficient tools in solute transport studies and management in porous media is usual. The present model by making use of the governing equation for two - dimensional solute transport in a porous medium expresses the ion adsorption effect on solute transport in a saturated semi infinite porous medium.This model has been solved analyticaly under the first type (Dirichlet condition) and the third type (Cauchy condition) boundary conditions. In solute transport by diffusion, the third type boundary condition shows the solute concentration at the boundary or on the soil surface more than that of the first boundary condition. In advection – dispersion solute transport, the solute concentration distribution curves resulting from the first and third boundary conditions because of advection predomination are approximately identical. The effect of ion adsorption on the medium,s solid matrix is evaluated by retardation factor in the model. The retardation factor greater than one causes retardation in solute transport, distribution and dispersion in the medium. The effect of ion adsorption on solute concentration distribution is independent of the selected boundary condition and depends on the modes of solute transport and dispersion in the medium, so as the rate of this effect in advection – dispersion solute transport is more than in diffusion transport.

In order to study the effect of water deficit stress and nitrogen on yield and compatibility metabolites in two hybrid corn cultivars, an experiment was conducted at the experimental farms of Tarbiat Modarres and Kermanshah Razi Universities. The experimental design was randomized complete block with treatments organized following a split-factorial with three replications. Drought stress at three growth stages (vegetative, reproductive and both vegetative and reproductive) and without water deficit or control were randomized to the main plot units. Combination of two levels of nitrogen fertilizer (100 and 200 kg/ha) and two maize varieties (S.C. 647 and T.C. 647) were appled to the subplot units. The results showed that water deficit stress, nitrogen and cultivar had significant effects on grain yield. Water deficit stress at vegetative phase had significant effect on leaf soluble proteins and was reduced in stress treatments as compared with the control. Soluble protein in the treatments with full fertilizer was more than nitrogen deficit treatments. At the end of each phase of stress (growth stage) water deficit induced a significant increase of proline in leaves. Nitrogen levels showed positive correlation with the loaf proline Content. Water deficit stress led to a significant decrease of chlorophyll content in examined cultivars. Water deficit had a significant effect on free amino acid content of leaf at the end of reproductive stage.

Generally irrigation efficiency is reported too low in most fields of Iran. It can be increased when the design input parameters such as infiltration opportunity and advance time are estimated accurately. Volume balance model (VBM) is introduced as an appropriate model for evaluation of the surface irrigation system and its application in designing the irrigation system. However, the predicted values from this model are not strongly correlated to the hydraulic model due to the model assumptions. The purpose of this research was to develop an improved volume balance model (IVBM) for furrow irrigation system. The IVBM is based on a combination of the VBM and the Zero-Inertia model (ZIM) that was used to desing border irrigation system. For evaluation of the model, it used five series of field data with a differente range of soil texture and the field properties. The data included geometric/hydraulics coefficients, advance time, inflow/outflow discharges, Manning’s coefficient, bed slope and Kostiakov infiltration coefficient. The comparison of the models (VBM and IVBM) has been done with root mean squared error (RMSE) with the observed data. The result shows an increasing of 15-40% accuracy for advance distance estimation of IVBM in comparison with the VBM.

Cylindrical weir is one of the broad-crested weirs used extensively due to steady state flow pattern and free passing of suspended materials over it. In this study, flow conditions over five physical models of cylindrical weir were tested and the effects of parameters such as weir diameter, hydraulic head at the upstream side and negative pressure on the weir were investigated. The flow was also simulated using turbulence model by Fluent software and the results were compared with those from the physical model. There was a rise in coefficient of discharge when the total head of flow and negative pressure over the weir were increased. The average relative errors of Fluent software in simulation of upstream flow depth, depth of flow over the weir and coefficient of discharge were 0.914%, 1.39% and 4.172%, respectively. By considering the hydraulic properties of flow over the cylindrical weir, a good agreement between the results of Fluent software and physical model was confirmed.

In water construction projects, river engineering, and irrigation and drainage engineering, it is vital to estimate the accurate volume of the sediment transported by rivers. As the sediment transport phenomenon is an immensely complex problem, therefore presenting an appropriate solution for precise evaluation of the suspended load in rivers is tedious and the mathematical models are not also accurate enough to be applied. Nowadays application of artificial intelligence systems has been developed as a novel solution in analysis of water resources problems. In this research, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Artificial Neural Networks (ANNs) models were utilized to determine suspended sediment rate of Ajichay River. Discharge, sediment load and water level data were used to prepare the models and obtain sediment rating curves. The statistical period is also divided into three seasons, namely dry, wet and snow melting. The accuracy of the models for these periods has been tested. The results showed that ANFIS neuro-fuzzy had better accuracy for determination of suspend sediment loads in comparison with both the ANNS and the rating curve.

Hydraulic jump has been used for dissipation of kinetic energy downstream of hydraulic structures such as spillways, chutes and gates. It is found by many researches that if jumps are made to occur on a corrugated bed, tail water and length of jumps will be reduced significantly. During formation of hydraulic jump on a corrugated bed the flow is turbulent, with water and air being mixed together. In the present study numerical simulations of hydraulic jump on corrugated bed were done by the CFD's FLUENT software in two dimensions using standard k-ε and RNG k-ε models. The free surface was determined using the VOF method. The results showed that the k-ε turbulent models and VOF method for predicting water surface in the jump on the corrugated bed were suitable and the relative error of the predicted water surface profiles and measured values were within 2-7 %. The study of the axial velocity profiles at different sections in the jump showed that velocity profiles in different experiments were similar and the agreement between the experimental and numerical results was satisfactory. Also the effects of corrugations on the basic characteristics of the jump such as free surface location, velocity and shear stress distributions were studied for different values of Froude number.

Necessity of river flow forecasting in constructional works, planning for optimal usage of water reservoirs, river training and flood warning has been well recognized. In this regard, the rainfall – runoff process has been widely studied using artificial neural networks modeling. In the current research, multi layer perceptron was applied to forecasting rainfall – runoff of Lighvan Chai snowy basin in East Azarbaijan province. The data of the basin includes daily rainfall, temperature, and runoff which their effects on the efficiency of network were studied at different steps. Getting along with the factors of rainfall and temperature at the current day, previous days and runoff in previous days in entrance matrix has led to the best results for neural networks. As the Lighvan Chai is a snowy basin, the effect of temperature and snowmelt on runoff is very important and a new neuron which is called conditional neuron of threshold temperature was introduced. Figure of this neuron is binary and the numbers are zero – one. The snowmelt temperature is the criterion of using these numbers. The results of neural networks model was compared to those from the dimensionless snowmelt hydrograph (DSH) including a greater efficiency of the neural networks.

In hydroponic cultures, the effects of nitrogen form and pH of nutrient solution on the chemical composition of plants are important. In order to study their effects on the uptake and concentrations of K, Ca, Mg and Na in root and shoot of spinach plant, a factorial experiment was conducted with two factors including nutrient solution pH at three levels (4.5, 6.5 and 8) and nitrate to ammonium ratio in nutrient solution at five levels (100:0, 75:25, 50:50, 25:75 and 0:100). The experiment was carried out in a completely randomized design with four replications in perlite culture under greenhouse conditions. Three seedlings of spinach plant (Spinacia oleracea L. Sirius cultivar) were cultivated per pot and with 15 different nutrient solutions were nourished. The concentrations of nitrogen and other nutrients in all nutrient solutions were identical. The plants were harvested after seven weeks and spinach root and shoot concentrations of K, Ca, Mg and Na were measured by wet digestion method. Results showed that the effects of nutrient solution pH on the root and shoot concentrations of K, root concentration of Ca, Ca uptake, shoot concentration of Mg were significant, but on the root and shoot concentrations of Na, Na uptake, root concentration of Mg, and Mg uptake were not. By increasing the nutrient solution pH from 4.5 to 8, K and Ca uptake by plant, and root concentration of K significantly decreased, while shoot concentrations of K and Mg, and root concentration of Ca significantly increased. The main effect of nitrate to ammonium ratio in nutrient solution, and the interactive effects of pH and the ratio of nitrate to ammonium on the root and shoot concentrations of K, Ca, Mg and Na, and their uptake by plant beca,e significant. In general, by decreasing the nitrate to ammonium ratio in nutrient solution, the root and shoot concentrations of K, Ca, Mg and Na, and their uptake by spinach plant became significantly decreased. Also, the effect of nitrate to ammonium ratio of nutrient solution on the root and shoot concentrations of K, Ca, Mg and Na, and their uptake by spinach plant was dependent on the nutrient solution pH and vice versa. The average shoot concentrations of K, Ca, Mg and Na were higher than those in root. There were significant correlations between the shoot and root concentrations of these elements, and also between the elements concentrations in the shoot and their uptake by spinach plant.

Kinetics of native Mn desorption by diethlenetriaminepentaacetic acid (DTPA) from 12 calcareous soils of Iran was studied in a laboratory experiment. Comparisons of coefficients of determination (r) and standard errors (SE) for seven different models indicated that the two best models for describing the desorption data in all soils were two-constant rate and parabolic – diffusion equations. The zero, first, second and third order equations could not describe Mn desorption properly and the coefficients of determination (r) decreased from zero to third order. Organic carbon and clay contents were the most pertinent soil characteristics that predicted the rate constants of the two constant and parabolic kinetic models. The amount of extractable Mn for long extraction times (the constant ˝a˝) increased with organic matter content. Constant ˝a˝ ranged from 0.585 to 11.24 (mgMn kg-1min-1) and ˝b˝ from o.313 to 0.554(mgMn kg-1min-1)-1. The rate at which Mn was extracted from each soil (related to the constant b) were quite variable and was not systematically related to any of the measured soil properties. Consequently a soil test for available Mn involving extraction for a quite short time period might underestimate the amount of Mn that was released in reality.

Determination of flow resistance is an essential parameter for studying and hydraulic analyzing open channels. Flow resistance in channes with coarse materials on bed is mostly due to grain roughness. Over the past decades several studies have been conducted to correlate the flow resistance to the particle size; however the effects of particle shapes are not clear so far. Therefore, it is the purpose of this work to conduct experimental studies to see how important the type and size of coarse sediments are. In this study eight types of sediments; four rounded and four angular sediments have been tested under different flow conditions in a flume of 10 meter long, 25 cm wide and 50 cm height. Experiments were conducted under constant bed slope of 0.0005. Results show that parameters such as relative roughness., Froude number,, and channel shape factor, play important roles in accurate estimation of flow resistance. The results have also shown that in gravelbed rivers the flow resistance can be estimated by semi logarithmic and power equations with the same accuracy. In this study an equation was also proposed for prediction of flow resistance coefficient

In this study, evaluation of SWAP model in simulating water table fluctuations and subsurface drainage rate was investigated at the wheat- grain maize cultivated unit in the Voshmgir irrigation network, Golestan province. Water table levels and drainage rate data were monitored in the study area at different times during the evaluation period. The statistical comparison based on the root mean square error (RMSE) showed the values of 9.55 cm and 0.22 mm/day for the estimated ground water depth and drainage flow rate, respectively. The simulated groundwater levels and subsurface drainage rate agreed well with those measured values. The analysis of the estimated bottom flux by the model and comparison of the measured and simulated water table depths showed that there was a negligible water exchange between the shallow groundwater and the soil profile beneath the impermeable layer. For analysis of managerial scenarios, no flow condition at the bottom of the soil profile, and zero bottom flux can be applied as the bottom boundary conditions. Therefore, the SWAP is a powerful model in estimating hydrological components of drainage system and drain water management in irrigated areas.

This study aims to compare the ability of dynamic artificial neural network (DANN) and multivariate linear regression (LR) in forecasting monthly inflow to Shahcheraghi reservoir in Semnan province, Iran. The input data consisted monthly flow discharge, precipitation, mean temperature and snow cover area. Snow cover area was estimated using NOAA-AVHRR images, based on thresholds in histograms of different phenomena in visible and thermal channels. Dynamic artificial neural networks were determined with one hidden layer, Levenberg-Marquardt as training function, and sigmoid as transfer function Moreover, five DANN and five LR models were run with different input data and the results were compared. Root mean square (RMSE), mean bias error (MBE), mean absolute relative error (MARE), maximum relative error (REmax) and R2 (coefficient of determination) are the criteria that were used for models evaluation. The best result is gained with three inputs (inflow discharge, precipitation and snow cover area) by DANN. Regarding linear regression as a classic model in inflow forecasting, the improvement of the results by using DANN was obvious. The REmax of the selected DANN model was almost 85% less than REmax of the selected LR.

Irrigation requirement 15 equal to the difference between water requirement and effective rainfall. The values of water irrigation requirements were reported in NETWAT program for various crops at different regions of Iran. However, the mean values were used in that program, therefore, the values of water and irrigation requirements have probability level of 50 percent. In this study, the values of mean irrigation requirement (probability level of 50 percent) and critical irrigation requirement (high evapotranspiration and low rainfall) were predicted for most important agricultural crops in some regions of Fars province such as Abadeh, Eghlid, Darab, Dorodzan Dam, Zarghan, Shiraz, Fasa and Lar. Results showed that the reported values of irrigation requirement by NETWAT program were lower than the values of mean irrigation requirement at 88.5 percent of the cases. Also, the results indicated that the reported values of irrigation requirement by NETWAT program were lower than the values of critical irrigation requirement at 98.1 percent of conditions. Therefore, it is possible to determine the values of mean and critical irrigation requirement (high evapotranspiration and low rainfall) of important agricultural crops in Fars province.